The first genuine leadership transition - moving from individual technical excellence to creating conditions where a whole team can perform. Your code matters less; your decisions, your culture, and your people development matter more.
Leading Through Others
Your primary output shifts from code to team performance. This means investing in the technical growth of every engineer on your team, making space for others to solve problems rather than solving them yourself, and measuring your success by what the team ships - not what you personally build.
Running Delivery, Not Just Doing It
TTLs own the delivery rhythm - sprint planning, backlog health, dependency management, and escalation when things go wrong. You need to develop a clear view of what's in flight, what's at risk, and what needs a decision at any given moment.
Giving and Receiving Feedback
Structured, timely feedback becomes a core tool. You will need to give feedback that is specific and actionable - on code, on behaviour, and on professional growth - and create an environment where feedback flows both ways without it feeling threatening.
Technical Credibility Under Pressure
You still need to be technically excellent, but now you must exercise that expertise in service of the team rather than in competition with it. That means knowing when to step in, when to coach, and when to let someone struggle productively.
Managing Up and Across
You are now a conduit between engineers and senior leadership. You need to represent your team's capacity, surface risks early, push back on unrealistic expectations with evidence, and translate business context into engineering direction.
Skills to Develop
Behaviours to Demonstrate
Use AI to draft structured 1:1 agendas and performance notes, then refine them with your own direct observations to ensure they reflect actual behaviour rather than generic patterns.
Leverage AI to generate multiple options for decomposing a complex engineering initiative, then evaluate each option against your team's actual capacity and skill set.
Use AI to summarise technical incident timelines and draft post-mortems quickly - always reviewing for factual accuracy before sharing with stakeholders.
Experiment with AI-assisted sprint planning to identify potential blockers and dependencies, treating outputs as prompts for team discussion rather than final answers.
Build awareness of AI's limitations in people contexts - AI cannot reliably predict team dynamics, individual motivation, or cultural fit, and using it for these judgements is dangerous.
Model responsible AI use for your team - demonstrating when AI accelerates good work and when human judgement must override AI suggestions.
The Manager's Path
The clearest guide to the tech lead transition - what changes, what stays the same, and what trips people up.
An Elegant Puzzle
Practical frameworks for engineering management and leading technical teams through ambiguity.
Radical Candor
How to give feedback that is both caring and direct - the skill most new leads struggle with first.
The Five Dysfunctions of a Team
A model for diagnosing and fixing team dysfunction that every lead should internalise.
Accelerate
The research-backed case for what high-performing engineering teams actually look like and how to get there.
Engineering Management Fundamentals
Short, practitioner-led content specifically designed for the IC-to-lead transition.
Crucial Conversations
Practical frameworks for high-stakes conversations - performance, conflict, and direction-setting.
DORA State of DevOps Report
Understand the metrics and capabilities that distinguish high-performing teams from the rest.
Review the full expectations for both roles to understand exactly what good looks like at each level.
→ Senior Software Engineer Archetype → Technical Team Lead Archetype